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cs.LG2023★ 1 cited
Hard Adversarial Example Mining for Improving Robust Fairness
Chenhao Lin, Xiang Ji, Yulong Yang +4
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Neverth…
cs.LG2023
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Xuanqi Gao, Juan Zhai, Shiqing Ma +3
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Cl…